{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABU量化系统使用文档 \n",
    "\n",
    "<center>\n",
    "        <img src=\"./image/abu_logo.png\" alt=\"\" style=\"vertical-align:middle;padding:10px 20px;\"><font size=\"6\" color=\"black\"><b>第8节 A股市场的回测</b></font>\n",
    "</center>\n",
    "\n",
    "-----------------\n",
    "\n",
    "作者: 阿布\n",
    "\n",
    "阿布量化版权所有 未经允许 禁止转载\n",
    "\n",
    "[abu量化系统github地址](https://github.com/bbfamily/abu) (您的star是我的动力！)\n",
    "\n",
    "[本节ipython notebook](https://github.com/bbfamily/abu/tree/master/abupy_lecture)\n",
    "\n",
    "之前的小节回测示例都是使用美股，本节示例A股市场的回测。\n",
    "\n",
    "首先导入abupy中本节使用的模块："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "enable example env will only read RomDataBu/df_kl.h5\n"
     ]
    }
   ],
   "source": [
    "# 基础库导入\n",
    "\n",
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import sys\n",
    "# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题\n",
    "sys.path.insert(0, os.path.abspath('../'))\n",
    "import abupy\n",
    "\n",
    "# 使用沙盒数据，目的是和书中一样的数据环境\n",
    "abupy.env.enable_example_env_ipython()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "买入因子，卖出因子等依然使用相同的设置，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from abupy import AbuFactorAtrNStop, AbuFactorPreAtrNStop, AbuFactorCloseAtrNStop, AbuFactorBuyBreak\n",
    "from abupy import abu, EMarketTargetType, AbuMetricsBase, ABuMarketDrawing, ABuProgress, ABuSymbolPd\n",
    "\n",
    "# 设置初始资金数\n",
    "read_cash = 1000000\n",
    "\n",
    "# 买入因子依然延用向上突破因子\n",
    "buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},\n",
    "               {'xd': 42, 'class': AbuFactorBuyBreak}]\n",
    "\n",
    "# 卖出因子继续使用上一节使用的因子\n",
    "sell_factors = [\n",
    "    {'stop_loss_n': 1.0, 'stop_win_n': 3.0,\n",
    "     'class': AbuFactorAtrNStop},\n",
    "    {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5},\n",
    "    {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. A股市场的回测示例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "择时股票池使用沙盒缓存数据中的如下股票：\n",
    "\n",
    "#### A股市场：\n",
    "\n",
    "* 科大讯飞(002230)\n",
    "* 乐视网(300104)\n",
    "* 东方财富(300059)\n",
    "* 中国中车(601766)\n",
    "* 同仁堂(600085),\n",
    "* 招商银行(600036)\n",
    "* 山西汾酒(600809)\n",
    "* 万科A(000002)\n",
    "* 比亚迪(002594)\n",
    "* 万达电影(002739)\n",
    "* 上证指数(sh000001)\n",
    "\n",
    "代码如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 择时股票池\n",
    "choice_symbols = ['002230', '300104', '300059', '601766', '600085', '600036', '600809', '000002', '002594', '002739']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 使用run_loop_back运行策略\n",
    "abu_result_tuple, kl_pd_manger = abu.run_loop_back(read_cash,\n",
    "                                                   buy_factors,\n",
    "                                                   sell_factors,\n",
    "                                                   n_folds=6,\n",
    "                                                   choice_symbols=choice_symbols)\n",
    "ABuProgress.clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "买入后卖出的交易数量:169\n",
      "买入后尚未卖出的交易数量:1\n",
      "胜率:55.6213%\n",
      "平均获利期望:17.4736%\n",
      "平均亏损期望:-6.6848%\n",
      "盈亏比:3.5702\n",
      "策略收益: 164.6985%\n",
      "基准收益: 75.7668%\n",
      "策略年化收益: 41.1746%\n",
      "基准年化收益: 18.9417%\n",
      "策略买入成交比例:88.2353%\n",
      "策略资金利用率比例:34.8566%\n",
      "策略共执行1008个交易日\n"
     ]
    },
    {
     "data": {
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dpmku6LBsOPC3DqvNAq4zTfMhwzCWAw2ty7eapvm9Pqr3oDrmmOO4997f8847b5KWlobN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\nhY2IiIiIiIx4/x/7lRp68pjKYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114927438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<abupy.MetricsBu.ABuMetricsBase.AbuMetricsBase at 0x10c9ed128>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AbuMetricsBase.show_general(*abu_result_tuple, only_show_returns=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的回测结果虽然可以正常运行，但是很多交易细节还是使用的默认设置中的美股交易模式，因为默认的设置EMarketTargetType.E_MARKET_TARGET_US是美股，它会影响到一年多少个交易日等等交易细节，基准标尺等问题，如注意观察上面使用使用show_general显示的最终收益对比图，可以发现策略收益对比的是纳斯达克指数，并不是A股大盘。\n",
    "\n",
    "正确的做法是首先将abupy量化环境设置为A股，代码如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_CN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "abu_result_tuple, kl_pd_manger = abu.run_loop_back(read_cash,\n",
    "                                                   buy_factors,\n",
    "                                                   sell_factors,\n",
    "                                                   n_folds=6,\n",
    "                                                   choice_symbols=choice_symbols)\n",
    "ABuProgress.clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "买入后卖出的交易数量:252\n",
      "买入后尚未卖出的交易数量:2\n",
      "胜率:43.6508%\n",
      "平均获利期望:17.5752%\n",
      "平均亏损期望:-6.9084%\n",
      "盈亏比:2.1424\n",
      "策略收益: 114.8238%\n",
      "基准收益: 20.6036%\n",
      "策略年化收益: 19.8870%\n",
      "基准年化收益: 3.5685%\n",
      "策略买入成交比例:83.0709%\n",
      "策略资金利用率比例:38.6073%\n",
      "策略共执行1455个交易日\n"
     ]
    },
    {
     "data": {
      "image/png": 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EZ599iocf/l3Kc/XfhM4VF7s6rE2pYiMiIiLSDzxBDwAue3bS/gxbc8Um3L8Vm2g0ymdV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ZBdVIdep2Px1Ci+2d1zccZnP95PSnzwgEZusovr2JhWTHykP2+uOURzq52FKZHMmBBGWKAPJVVN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\nzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148f7588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<abupy.MetricsBu.ABuMetricsBase.AbuMetricsBase at 0x11572afd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AbuMetricsBase.show_general(*abu_result_tuple, only_show_returns=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 涨跌停的特殊处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面主要讲解一下A股市场中比较特殊的地方：涨停，跌停，首先看一下下面这笔交易："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buy_date</th>\n",
       "      <th>buy_price</th>\n",
       "      <th>buy_cnt</th>\n",
       "      <th>buy_factor</th>\n",
       "      <th>symbol</th>\n",
       "      <th>buy_pos</th>\n",
       "      <th>buy_type_str</th>\n",
       "      <th>expect_direction</th>\n",
       "      <th>sell_type_extra</th>\n",
       "      <th>sell_date</th>\n",
       "      <th>sell_price</th>\n",
       "      <th>sell_type</th>\n",
       "      <th>ml_features</th>\n",
       "      <th>key</th>\n",
       "      <th>profit</th>\n",
       "      <th>result</th>\n",
       "      <th>profit_cg</th>\n",
       "      <th>profit_cg_hunder</th>\n",
       "      <th>keep_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-04-17</th>\n",
       "      <td>20150417</td>\n",
       "      <td>35.61</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>AbuFactorBuyBreak:60</td>\n",
       "      <td>601766</td>\n",
       "      <td>AbuAtrPosition</td>\n",
       "      <td>call</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AbuFactorAtrNStop:stop_loss=1.0</td>\n",
       "      <td>20150608</td>\n",
       "      <td>32.13</td>\n",
       "      <td>loss</td>\n",
       "      <td>None</td>\n",
       "      <td>900</td>\n",
       "      <td>-8352.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.0977</td>\n",
       "      <td>-9.7725</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            buy_date  buy_price  buy_cnt            buy_factor  symbol  \\\n",
       "2015-04-17  20150417      35.61   2400.0  AbuFactorBuyBreak:60  601766   \n",
       "\n",
       "                   buy_pos buy_type_str  expect_direction  \\\n",
       "2015-04-17  AbuAtrPosition         call               1.0   \n",
       "\n",
       "                            sell_type_extra  sell_date  sell_price sell_type  \\\n",
       "2015-04-17  AbuFactorAtrNStop:stop_loss=1.0   20150608       32.13      loss   \n",
       "\n",
       "           ml_features  key  profit  result  profit_cg  profit_cg_hunder  \\\n",
       "2015-04-17        None  900 -8352.0      -1    -0.0977           -9.7725   \n",
       "\n",
       "            keep_days  \n",
       "2015-04-17         52  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_pd = abu_result_tuple.orders_pd\n",
    "view_orders = orders_pd[(orders_pd['symbol'] == '601766') & (orders_pd['buy_date'] == 20150417)]\n",
    "view_orders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从交易单来看似乎一切ok，虽然最终交易亏损了，下面使用plot_candle_from_order可视化view_orders，plot_candle_from_order标记出了买入和卖出点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RAKyR3dIAANhIWt6eVhTF5iR3JxlJclGSX0jy35N8PMmZJEeS/FhZlnOtjgUA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\nk/KPRSNJUpfwxESSlG1+FklS13J4miRJkiRJkpLY00g9jr9AKZuGzN3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JLA0WjXJUCGF3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\n/gAUrne6550ErQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11505ab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trade_df = ABuMarketDrawing.plot_candle_from_order(view_orders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ABuMarketDrawing.plot_candle_from_order返回的trade_df是这笔交易的持股周期内的金融时间序列，如下所示，看第一条数据\n",
    "2015-04-17交易日即为买入交易日，可以发现close，high，low的价格都是一样的，这代表了在集合竞价阶段股票已经涨停，但在我们回测中默认使用的\n",
    "滑点买入类依然认为可以买入。\n",
    "\n",
    "备注：滑点类相关内容请阅读：滑点策略与交易手续费"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "买入出价格为：35.61\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-04-17</th>\n",
       "      <td>35.61</td>\n",
       "      <td>35.61</td>\n",
       "      <td>35.61</td>\n",
       "      <td>10.077</td>\n",
       "      <td>35.61</td>\n",
       "      <td>32.35</td>\n",
       "      <td>38949900</td>\n",
       "      <td>20150417</td>\n",
       "      <td>4</td>\n",
       "      <td>63</td>\n",
       "      <td>2.9390</td>\n",
       "      <td>3.5273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-20</th>\n",
       "      <td>35.11</td>\n",
       "      <td>39.20</td>\n",
       "      <td>32.61</td>\n",
       "      <td>-1.404</td>\n",
       "      <td>39.20</td>\n",
       "      <td>35.61</td>\n",
       "      <td>843953322</td>\n",
       "      <td>20150420</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>3.2953</td>\n",
       "      <td>3.9703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-21</th>\n",
       "      <td>31.57</td>\n",
       "      <td>34.00</td>\n",
       "      <td>31.57</td>\n",
       "      <td>-10.083</td>\n",
       "      <td>33.83</td>\n",
       "      <td>35.11</td>\n",
       "      <td>357461681</td>\n",
       "      <td>20150421</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>3.3631</td>\n",
       "      <td>3.9796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-22</th>\n",
       "      <td>29.99</td>\n",
       "      <td>31.57</td>\n",
       "      <td>28.39</td>\n",
       "      <td>-5.005</td>\n",
       "      <td>28.64</td>\n",
       "      <td>31.57</td>\n",
       "      <td>773508495</td>\n",
       "      <td>20150422</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>3.6688</td>\n",
       "      <td>4.3450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-23</th>\n",
       "      <td>30.60</td>\n",
       "      <td>31.41</td>\n",
       "      <td>29.23</td>\n",
       "      <td>2.034</td>\n",
       "      <td>29.53</td>\n",
       "      <td>29.99</td>\n",
       "      <td>513939670</td>\n",
       "      <td>20150423</td>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>3.5478</td>\n",
       "      <td>4.0777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close   high    low  p_change   open  pre_close     volume  \\\n",
       "2015-04-17  35.61  35.61  35.61    10.077  35.61      32.35   38949900   \n",
       "2015-04-20  35.11  39.20  32.61    -1.404  39.20      35.61  843953322   \n",
       "2015-04-21  31.57  34.00  31.57   -10.083  33.83      35.11  357461681   \n",
       "2015-04-22  29.99  31.57  28.39    -5.005  28.64      31.57  773508495   \n",
       "2015-04-23  30.60  31.41  29.23     2.034  29.53      29.99  513939670   \n",
       "\n",
       "                date  date_week  key   atr21   atr14  \n",
       "2015-04-17  20150417          4   63  2.9390  3.5273  \n",
       "2015-04-20  20150420          0   64  3.2953  3.9703  \n",
       "2015-04-21  20150421          1   65  3.3631  3.9796  \n",
       "2015-04-22  20150422          2   66  3.6688  4.3450  \n",
       "2015-04-23  20150423          3   67  3.5478  4.0777  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('买入价格为：{}'.format(view_orders.iloc[0].buy_price))\n",
    "trade_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和买入类似，注意下面这笔交易："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ki5RM/LXcc5dOv+maZIJ0qdT7M3fhVkWmXtSTxw7o2o7r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\no9uTHJbkmlLKAUkW9kyoquriJBcnSSnlL5K8alOBUZIsXbqyboX21t4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a/eVwSpL/lRi7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\nuGgkSZIkSZKkIf4Bxv+fmVK1etkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e602b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "卖出价格为：21.27\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-15</th>\n",
       "      <td>20.36</td>\n",
       "      <td>20.74</td>\n",
       "      <td>18.88</td>\n",
       "      <td>5.165</td>\n",
       "      <td>18.88</td>\n",
       "      <td>19.36</td>\n",
       "      <td>167387632</td>\n",
       "      <td>20151215</td>\n",
       "      <td>1</td>\n",
       "      <td>104</td>\n",
       "      <td>1.4807</td>\n",
       "      <td>1.6874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-16</th>\n",
       "      <td>19.47</td>\n",
       "      <td>20.27</td>\n",
       "      <td>19.29</td>\n",
       "      <td>-4.371</td>\n",
       "      <td>19.91</td>\n",
       "      <td>20.36</td>\n",
       "      <td>118536396</td>\n",
       "      <td>20151216</td>\n",
       "      <td>2</td>\n",
       "      <td>105</td>\n",
       "      <td>1.4352</td>\n",
       "      <td>1.5931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17</th>\n",
       "      <td>21.49</td>\n",
       "      <td>21.49</td>\n",
       "      <td>19.63</td>\n",
       "      <td>10.375</td>\n",
       "      <td>19.66</td>\n",
       "      <td>19.47</td>\n",
       "      <td>258339265</td>\n",
       "      <td>20151217</td>\n",
       "      <td>3</td>\n",
       "      <td>106</td>\n",
       "      <td>1.4738</td>\n",
       "      <td>1.6286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18</th>\n",
       "      <td>23.71</td>\n",
       "      <td>23.71</td>\n",
       "      <td>20.94</td>\n",
       "      <td>10.330</td>\n",
       "      <td>21.68</td>\n",
       "      <td>21.49</td>\n",
       "      <td>223898402</td>\n",
       "      <td>20151218</td>\n",
       "      <td>4</td>\n",
       "      <td>107</td>\n",
       "      <td>1.7253</td>\n",
       "      <td>1.9768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-04</th>\n",
       "      <td>21.27</td>\n",
       "      <td>21.27</td>\n",
       "      <td>21.27</td>\n",
       "      <td>-10.291</td>\n",
       "      <td>21.27</td>\n",
       "      <td>23.71</td>\n",
       "      <td>4266300</td>\n",
       "      <td>20160704</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1.5884</td>\n",
       "      <td>1.7426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close   high    low  p_change   open  pre_close     volume  \\\n",
       "2015-12-15  20.36  20.74  18.88     5.165  18.88      19.36  167387632   \n",
       "2015-12-16  19.47  20.27  19.29    -4.371  19.91      20.36  118536396   \n",
       "2015-12-17  21.49  21.49  19.63    10.375  19.66      19.47  258339265   \n",
       "2015-12-18  23.71  23.71  20.94    10.330  21.68      21.49  223898402   \n",
       "2016-07-04  21.27  21.27  21.27   -10.291  21.27      23.71    4266300   \n",
       "\n",
       "                date  date_week  key   atr21   atr14  \n",
       "2015-12-15  20151215          1  104  1.4807  1.6874  \n",
       "2015-12-16  20151216          2  105  1.4352  1.5931  \n",
       "2015-12-17  20151217          3  106  1.4738  1.6286  \n",
       "2015-12-18  20151218          4  107  1.7253  1.9768  \n",
       "2016-07-04  20160704          0  108  1.5884  1.7426  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view_orders = orders_pd[(orders_pd['symbol'] == '000002') & (orders_pd['sell_date'] == 20160704)]\n",
    "trade_df = ABuMarketDrawing.plot_candle_from_order(view_orders.iloc[0])\n",
    "print('卖出价格为：{}'.format(view_orders.iloc[0].sell_price))\n",
    "trade_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面ABuMarketDrawing.plot_candle_from_order返回的trade_df是这笔交易的持股周期内的金融时间序列，看第最后一条数据\n",
    "2016-07-04交易日即为卖出交易日，可以发现close，high，low的价格都是一样的，这代表了在集合竞价阶段股票已经跌停，但在我们回测中默认使用的\n",
    "滑点卖出类依然认为可以卖出。\n",
    "\n",
    "类似的情况还有下面这种虽然并不是在集合竞价阶段股票涨停，但是涨停下依然使用当天最高最低均价买入，买入价格为：1.175显然也不合适，同理在非集合竞价跌停的情况下以当天的平均价格卖出也不合适。\n",
    "\n",
    "备注：默认滑点类使用均价买入卖出，详情阅读AbuSlippageSellBase，AbuSlippageBuyBase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vAOgBX7h0cd70ywYqec/efQsu+75PfjIvO39+3muPN72f7mtMZD1TTMMYEYDO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a2192e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "买入价格为：1.175\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-02-22</th>\n",
       "      <td>1.23</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.12</td>\n",
       "      <td>11.818</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.1</td>\n",
       "      <td>15130356</td>\n",
       "      <td>20120222</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>0.0771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close  high   low  p_change  open  pre_close    volume      date  \\\n",
       "2012-02-22   1.23  1.23  1.12    11.818  1.12        1.1  15130356  20120222   \n",
       "\n",
       "            date_week  key   atr21   atr14  \n",
       "2012-02-22          2   79  0.0693  0.0771  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view_orders = orders_pd[(orders_pd['symbol'] == '300059') & (orders_pd['buy_date'] == 20120222)]\n",
    "trade_df = ABuMarketDrawing.plot_candle_from_order(view_orders.iloc[0])\n",
    "print('买入价格为：{}'.format(view_orders.iloc[0].buy_price))\n",
    "trade_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为解决上述问题abupy中有针对A股涨停和跌停的特殊装饰器封装在滑点模块中：\n",
    "\n",
    "具体实现原理不在这里展开，效果为：\n",
    "\n",
    "1. 针对非集合竞价阶段的涨停，滑点买入价格以高概率在接近涨停的价格买入\n",
    "2. 针对非集合竞价阶段的跌停，滑点卖出价格以高概率在接近跌停的价格卖出\n",
    "3. 集合竞价阶段的涨停根据设置中的买入成功概率进行买入决策\n",
    "3. 集合竞价阶段的跌停根据设置中的卖出成功概率进行卖出决策\n",
    "\n",
    "具体实现请阅读源代码AbuSlippageSellBase，AbuSlippageBuyBase。\n",
    "\n",
    "下面的代码即将上述4个针对A股涨停和跌停的特殊设置打开："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from abupy import slippage\n",
    "# 开启针对非集合竞价阶段的涨停，滑点买入价格以高概率在接近涨停的价格买入\n",
    "slippage.sbb.g_enable_limit_up = True\n",
    "# 将集合竞价阶段的涨停买入成功概率设置为0，如果设置为0.2即20%概率成功买入\n",
    "slippage.sbb.g_pre_limit_up_rate = 0\n",
    "# 开启针对非集合竞价阶段的跌停，滑点卖出价格以高概率在接近跌停的价格卖出\n",
    "slippage.ssb.g_enable_limit_down = True\n",
    "# 将集合竞价阶段的跌停卖出成功概率设置为0, 如果设置为0.2即20%概率成功卖出\n",
    "slippage.ssb.g_pre_limit_down_rate = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其它的回测因子等设置都不变，重新使用abu.run_loop_back进行回测，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "abu_result_slippage, kl_pd_manger = abu.run_loop_back(read_cash,\n",
    "                                                   buy_factors,\n",
    "                                                   sell_factors,\n",
    "                                                   n_folds=6,\n",
    "                                                   choice_symbols=choice_symbols)\n",
    "ABuProgress.clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "买入后卖出的交易数量:249\n",
      "买入后尚未卖出的交易数量:2\n",
      "胜率:42.9719%\n",
      "平均获利期望:16.4189%\n",
      "平均亏损期望:-6.8735%\n",
      "盈亏比:1.8920\n",
      "策略收益: 89.4554%\n",
      "基准收益: 20.6036%\n",
      "策略年化收益: 15.4933%\n",
      "基准年化收益: 3.5685%\n",
      "策略买入成交比例:82.4701%\n",
      "策略资金利用率比例:39.7315%\n",
      "策略共执行1455个交易日\n"
     ]
    },
    {
     "data": {
      "image/png": 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4GWdyyUSqAzWsrF+NaZo9ymT1RpsCGxERERnMAu2BTYE7GdjYCEWiuWxSTpim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UKYFZLO54VfuT0fYg7G22R2lXUeiF6nJyMsncO1WayIXUKgZwAPbnmUL4u+Zn3RJm7PuJG0kJQh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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114fb10b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<abupy.MetricsBu.ABuMetricsBase.AbuMetricsBase at 0x11525bac8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AbuMetricsBase.show_general(*abu_result_slippage, only_show_returns=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的度量显示买入后卖出的交易数量:249，之前没开启涨跌停时是252，即可知道有三笔交易由于开启了涨跌停没有进行买入，但是怎么能知道那些交易发生了变化呢?\n",
    "\n",
    "备注：读者可尝试使用 ABU量化系统使用文档－第九节 港股市场的回测 中讲解的AbuSDBreak对上面A股交易进行回测，度量结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 对多组交易结果进行分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AbuOrderPdProxy是abupy中内置的针对交易单对象进行并集，交集，差集等交易单分析使用的工具，通过EOrderSameRule使用不同的判断为是否相同使用的交易单规则，更多实现详情请阅读AbuOrderPdProxy源代码，下面示例使用:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order == order_slippage: False\n",
      "order > order_slippage: True\n"
     ]
    }
   ],
   "source": [
    "from abupy import AbuOrderPdProxy, EOrderSameRule\n",
    "\n",
    "orders_pd_slippage = abu_result_slippage.orders_pd\n",
    "# 通过orders_pd构造AbuOrderPdProxy\n",
    "proxy = AbuOrderPdProxy(orders_pd)\n",
    "with proxy.proxy_work(abu_result_slippage.orders_pd) as (order, order_slippage):\n",
    "    print('order == order_slippage: {}'.format(order == order_slippage))\n",
    "    print('order > order_slippage: {}'.format(order > order_slippage))\n",
    "    diff_a = order - order_slippage\n",
    "    diff_b = order_slippage - order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面对比一下两个差集的第一个数据，可以发现不同点是买入价格：\n",
    "\n",
    "* 未使用涨跌停控制的diff_a的交易依然是使用1.17的价格买入股票\n",
    "* 使用涨跌停控制的diff_b的交易使用接近涨停价格1.23的价格买入股票\n",
    "\n",
    "备注：读者可以输出diff_a，diff_b自行一个一个对比一下看看，这里不再详对"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buy_date</th>\n",
       "      <th>buy_price</th>\n",
       "      <th>buy_cnt</th>\n",
       "      <th>buy_factor</th>\n",
       "      <th>symbol</th>\n",
       "      <th>buy_pos</th>\n",
       "      <th>buy_type_str</th>\n",
       "      <th>expect_direction</th>\n",
       "      <th>sell_type_extra</th>\n",
       "      <th>sell_date</th>\n",
       "      <th>sell_price</th>\n",
       "      <th>sell_type</th>\n",
       "      <th>ml_features</th>\n",
       "      <th>key</th>\n",
       "      <th>profit</th>\n",
       "      <th>result</th>\n",
       "      <th>profit_cg</th>\n",
       "      <th>profit_cg_hunder</th>\n",
       "      <th>keep_days</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-02-22</th>\n",
       "      <td>20120222</td>\n",
       "      <td>1.175</td>\n",
       "      <td>96100.0</td>\n",
       "      <td>AbuFactorBuyBreak:42</td>\n",
       "      <td>300059</td>\n",
       "      <td>AbuAtrPosition</td>\n",
       "      <td>call</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AbuFactorPreAtrNStop:pre_atr=1.5</td>\n",
       "      <td>20120315</td>\n",
       "      <td>1.165</td>\n",
       "      <td>loss</td>\n",
       "      <td>None</td>\n",
       "      <td>136</td>\n",
       "      <td>-961.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.0085</td>\n",
       "      <td>-0.8511</td>\n",
       "      <td>22</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            buy_date  buy_price  buy_cnt            buy_factor  symbol  \\\n",
       "2012-02-22  20120222      1.175  96100.0  AbuFactorBuyBreak:42  300059   \n",
       "\n",
       "                   buy_pos buy_type_str  expect_direction  \\\n",
       "2012-02-22  AbuAtrPosition         call               1.0   \n",
       "\n",
       "                             sell_type_extra  sell_date  sell_price sell_type  \\\n",
       "2012-02-22  AbuFactorPreAtrNStop:pre_atr=1.5   20120315       1.165      loss   \n",
       "\n",
       "           ml_features  key  profit  result  profit_cg  profit_cg_hunder  \\\n",
       "2012-02-22        None  136  -961.0      -1    -0.0085           -0.8511   \n",
       "\n",
       "            keep_days difference  \n",
       "2012-02-22         22       True  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_a.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buy_date</th>\n",
       "      <th>buy_price</th>\n",
       "      <th>buy_cnt</th>\n",
       "      <th>buy_factor</th>\n",
       "      <th>symbol</th>\n",
       "      <th>buy_pos</th>\n",
       "      <th>buy_type_str</th>\n",
       "      <th>expect_direction</th>\n",
       "      <th>sell_type_extra</th>\n",
       "      <th>sell_date</th>\n",
       "      <th>sell_price</th>\n",
       "      <th>sell_type</th>\n",
       "      <th>ml_features</th>\n",
       "      <th>key</th>\n",
       "      <th>profit</th>\n",
       "      <th>result</th>\n",
       "      <th>profit_cg</th>\n",
       "      <th>profit_cg_hunder</th>\n",
       "      <th>keep_days</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-02-22</th>\n",
       "      <td>20120222</td>\n",
       "      <td>1.16</td>\n",
       "      <td>96100.0</td>\n",
       "      <td>AbuFactorBuyBreak:42</td>\n",
       "      <td>300059</td>\n",
       "      <td>AbuAtrPosition</td>\n",
       "      <td>call</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AbuFactorPreAtrNStop:pre_atr=1.5</td>\n",
       "      <td>20120315</td>\n",
       "      <td>1.165</td>\n",
       "      <td>win</td>\n",
       "      <td>None</td>\n",
       "      <td>136</td>\n",
       "      <td>480.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0043</td>\n",
       "      <td>0.431</td>\n",
       "      <td>22</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            buy_date  buy_price  buy_cnt            buy_factor  symbol  \\\n",
       "2012-02-22  20120222       1.16  96100.0  AbuFactorBuyBreak:42  300059   \n",
       "\n",
       "                   buy_pos buy_type_str  expect_direction  \\\n",
       "2012-02-22  AbuAtrPosition         call               1.0   \n",
       "\n",
       "                             sell_type_extra  sell_date  sell_price sell_type  \\\n",
       "2012-02-22  AbuFactorPreAtrNStop:pre_atr=1.5   20120315       1.165       win   \n",
       "\n",
       "           ml_features  key  profit  result  profit_cg  profit_cg_hunder  \\\n",
       "2012-02-22        None  136   480.5       1     0.0043             0.431   \n",
       "\n",
       "            keep_days difference  \n",
       "2012-02-22         22       True  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_b.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面通过EOrderSameRule.ORDER_SAME_BD做为AbuOrderPdProxy的参数，这样构造的AbuOrderPdProxy即切换了对交易单相同的规则：\n",
    "\n",
    "    class EOrderSameRule(Enum):\n",
    "        \"\"\"对order_pd中对order判断为是否相同使用的规则\"\"\"\n",
    "\n",
    "        \"\"\"order有相同的symbol和买入日期就认为是相同\"\"\"\n",
    "        ORDER_SAME_BD = 0\n",
    "        \"\"\"order有相同的symbol, 买入日期，和卖出日期，即不考虑价格，只要日期相同就相同\"\"\"\n",
    "        ORDER_SAME_BSD = 1\n",
    "        \"\"\"order有相同的symbol, 买入日期，相同的买入价格，即单子买入时刻都相同\"\"\"\n",
    "        ORDER_SAME_BDP = 2\n",
    "        \"\"\"order有相同的symbol, 买入日期, 买入价格, 并且相同的卖出日期和价格才认为是相同，即买入卖出时刻都相同\"\"\"\n",
    "        ORDER_SAME_BSPD = 3\n",
    "\n",
    "使用相同的symbol和买入日期就认为是相同的规则，结果如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buy_date</th>\n",
       "      <th>buy_price</th>\n",
       "      <th>buy_cnt</th>\n",
       "      <th>buy_factor</th>\n",
       "      <th>symbol</th>\n",
       "      <th>buy_pos</th>\n",
       "      <th>buy_type_str</th>\n",
       "      <th>expect_direction</th>\n",
       "      <th>sell_type_extra</th>\n",
       "      <th>sell_date</th>\n",
       "      <th>sell_price</th>\n",
       "      <th>sell_type</th>\n",
       "      <th>ml_features</th>\n",
       "      <th>key</th>\n",
       "      <th>profit</th>\n",
       "      <th>result</th>\n",
       "      <th>profit_cg</th>\n",
       "      <th>profit_cg_hunder</th>\n",
       "      <th>keep_days</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-10-09</th>\n",
       "      <td>20131009</td>\n",
       "      <td>17.44</td>\n",
       "      <td>11400.0</td>\n",
       "      <td>AbuFactorBuyBreak:42</td>\n",
       "      <td>300104</td>\n",
       "      <td>AbuAtrPosition</td>\n",
       "      <td>call</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AbuFactorCloseAtrNStop:close_atr_n=1.5</td>\n",
       "      <td>20131024</td>\n",
       "      <td>19.00</td>\n",
       "      <td>win</td>\n",
       "      <td>None</td>\n",
       "      <td>527</td>\n",
       "      <td>17784.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0894</td>\n",
       "      <td>8.9450</td>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-17</th>\n",
       "      <td>20150417</td>\n",
       "      <td>16.41</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>AbuFactorBuyBreak:60</td>\n",
       "      <td>300059</td>\n",
       "      <td>AbuAtrPosition</td>\n",
       "      <td>call</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AbuFactorAtrNStop:stop_win=3.0</td>\n",
       "      <td>20150430</td>\n",
       "      <td>37.55</td>\n",
       "      <td>win</td>\n",
       "      <td>None</td>\n",
       "      <td>900</td>\n",
       "      <td>255794.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2882</td>\n",
       "      <td>128.8239</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-17</th>\n",
       "      <td>20150417</td>\n",
       "      <td>35.61</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>AbuFactorBuyBreak:60</td>\n",
       "      <td>601766</td>\n",
       "      <td>AbuAtrPosition</td>\n",
       "      <td>call</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AbuFactorAtrNStop:stop_loss=1.0</td>\n",
       "      <td>20150608</td>\n",
       "      <td>32.13</td>\n",
       "      <td>loss</td>\n",
       "      <td>None</td>\n",
       "      <td>900</td>\n",
       "      <td>-8352.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.0977</td>\n",
       "      <td>-9.7725</td>\n",
       "      <td>52</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            buy_date  buy_price  buy_cnt            buy_factor  symbol  \\\n",
       "2013-10-09  20131009      17.44  11400.0  AbuFactorBuyBreak:42  300104   \n",
       "2015-04-17  20150417      16.41  12100.0  AbuFactorBuyBreak:60  300059   \n",
       "2015-04-17  20150417      35.61   2400.0  AbuFactorBuyBreak:60  601766   \n",
       "\n",
       "                   buy_pos buy_type_str  expect_direction  \\\n",
       "2013-10-09  AbuAtrPosition         call               1.0   \n",
       "2015-04-17  AbuAtrPosition         call               1.0   \n",
       "2015-04-17  AbuAtrPosition         call               1.0   \n",
       "\n",
       "                                   sell_type_extra  sell_date  sell_price  \\\n",
       "2013-10-09  AbuFactorCloseAtrNStop:close_atr_n=1.5   20131024       19.00   \n",
       "2015-04-17          AbuFactorAtrNStop:stop_win=3.0   20150430       37.55   \n",
       "2015-04-17         AbuFactorAtrNStop:stop_loss=1.0   20150608       32.13   \n",
       "\n",
       "           sell_type ml_features  key    profit  result  profit_cg  \\\n",
       "2013-10-09       win        None  527   17784.0       1     0.0894   \n",
       "2015-04-17       win        None  900  255794.0       1     1.2882   \n",
       "2015-04-17      loss        None  900   -8352.0      -1    -0.0977   \n",
       "\n",
       "            profit_cg_hunder  keep_days difference  \n",
       "2013-10-09            8.9450         15       True  \n",
       "2015-04-17          128.8239         13       True  \n",
       "2015-04-17           -9.7725         52       True  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proxy = AbuOrderPdProxy(orders_pd, EOrderSameRule.ORDER_SAME_BD)\n",
    "with proxy.proxy_work(abu_result_slippage.orders_pd) as (order, order_slippage):\n",
    "    diff_c = order - order_slippage\n",
    "diff_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-04-17</th>\n",
       "      <td>35.61</td>\n",
       "      <td>35.61</td>\n",
       "      <td>35.61</td>\n",
       "      <td>10.077</td>\n",
       "      <td>35.61</td>\n",
       "      <td>32.35</td>\n",
       "      <td>38949900</td>\n",
       "      <td>20150417</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close   high    low  p_change   open  pre_close    volume  \\\n",
       "2015-04-17  35.61  35.61  35.61    10.077  35.61      32.35  38949900   \n",
       "\n",
       "                date  date_week  key  atr21  atr14  \n",
       "2015-04-17  20150417          4    0      0      0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ABuSymbolPd.make_kl_df('601766', start='20150417', end='20150417')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-10-09</th>\n",
       "      <td>17.44</td>\n",
       "      <td>17.44</td>\n",
       "      <td>17.44</td>\n",
       "      <td>10.032</td>\n",
       "      <td>17.44</td>\n",
       "      <td>15.85</td>\n",
       "      <td>485750</td>\n",
       "      <td>20131009</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close   high    low  p_change   open  pre_close  volume      date  \\\n",
       "2013-10-09  17.44  17.44  17.44    10.032  17.44      15.85  485750  20131009   \n",
       "\n",
       "            date_week  key  atr21  atr14  \n",
       "2013-10-09          2    0      0      0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ABuSymbolPd.make_kl_df('300104', start='20131009', end='20131009')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到diff_c中展示的三个交易单都是在集合竞价阶段已涨停了的股票交易，在开启了涨跌停控制后这三笔交易都没有进行买入。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如上述使用的AbuOrderPdProxy等工具，abupy不仅仅提供了对交易进行回测的功能，有许多分析，统计，机器学习，以及可视化工具在项目中可以帮助你分析策略，分析回测结果，以及为产生新的策略产生基础阀值等功能，在之后的教程中将陆续讲解使用以及示例。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### abu量化文档目录章节\n",
    "\n",
    "1. [择时策略的开发](http://www.abuquant.com/lecture/lecture_1.html)\n",
    "2. [择时策略的优化](http://www.abuquant.com/lecture/lecture_2.html)\n",
    "3. [滑点策略与交易手续费](http://www.abuquant.com/lecture/lecture_3.html)\n",
    "4. [多支股票择时回测与仓位管理](http://www.abuquant.com/lecture/lecture_4.html)\n",
    "5. [选股策略的开发](http://www.abuquant.com/lecture/lecture_5.html)\n",
    "6. [回测结果的度量](http://www.abuquant.com/lecture/lecture_6.html)\n",
    "7. [寻找策略最优参数和评分](http://www.abuquant.com/lecture/lecture_7.html)\n",
    "8. [A股市场的回测](http://www.abuquant.com/lecture/lecture_8.html)\n",
    "9. [港股市场的回测](http://www.abuquant.com/lecture/lecture_9.html)\n",
    "10. [比特币，莱特币的回测](http://www.abuquant.com/lecture/lecture_10.html)\n",
    "11. [期货市场的回测](http://www.abuquant.com/lecture/lecture_11.html)\n",
    "12. [机器学习与比特币示例](http://www.abuquant.com/lecture/lecture_12.html)\n",
    "13. [量化技术分析应用](http://www.abuquant.com/lecture/lecture_13.html)\n",
    "14. [量化相关性分析应用](http://www.abuquant.com/lecture/lecture_14.html)\n",
    "15. [量化交易和搜索引擎](http://www.abuquant.com/lecture/lecture_15.html)\n",
    "16. [UMP主裁交易决策](http://www.abuquant.com/lecture/lecture_16.html)\n",
    "17. [UMP边裁交易决策](http://www.abuquant.com/lecture/lecture_17.html)\n",
    "18. [自定义裁判决策交易](http://www.abuquant.com/lecture/lecture_18.html)\n",
    "19. [数据源](http://www.abuquant.com/lecture/lecture_19.html)\n",
    "20. [A股全市场回测](http://www.abuquant.com/lecture/lecture_20.html)\n",
    "21. [A股UMP决策](http://www.abuquant.com/lecture/lecture_21.html)\n",
    "22. [美股全市场回测](http://www.abuquant.com/lecture/lecture_22.html)\n",
    "23. [美股UMP决策](http://www.abuquant.com/lecture/lecture_23.html)\n",
    "\n",
    "\n",
    "abu量化系统文档教程持续更新中，请关注公众号中的更新提醒。\n",
    "\n",
    "#### 《量化交易之路》目录章节及随书代码地址\n",
    "\n",
    "1. [第二章 量化语言——Python](https://github.com/bbfamily/abu/tree/master/ipython/第二章-量化语言——Python.ipynb)\n",
    "2. [第三章 量化工具——NumPy](https://github.com/bbfamily/abu/tree/master/ipython/第三章-量化工具——NumPy.ipynb)\n",
    "3. [第四章 量化工具——pandas](https://github.com/bbfamily/abu/tree/master/ipython/第四章-量化工具——pandas.ipynb)\n",
    "4. [第五章 量化工具——可视化](https://github.com/bbfamily/abu/tree/master/ipython/第五章-量化工具——可视化.ipynb)\n",
    "5. [第六章 量化工具——数学：你一生的追求到底能带来多少幸福](https://github.com/bbfamily/abu/tree/master/ipython/第六章-量化工具——数学.ipynb)\n",
    "6. [第七章 量化系统——入门：三只小猪股票投资的故事](https://github.com/bbfamily/abu/tree/master/ipython/第七章-量化系统——入门.ipynb)\n",
    "7. [第八章 量化系统——开发](https://github.com/bbfamily/abu/tree/master/ipython/第八章-量化系统——开发.ipynb)\n",
    "8. [第九章 量化系统——度量与优化](https://github.com/bbfamily/abu/tree/master/ipython/第九章-量化系统——度量与优化.ipynb)\n",
    "9. [第十章 量化系统——机器学习•猪老三](https://github.com/bbfamily/abu/tree/master/ipython/第十章-量化系统——机器学习•猪老三.ipynb)\n",
    "10. [第十一章 量化系统——机器学习•ABU](https://github.com/bbfamily/abu/tree/master/ipython/第十一章-量化系统——机器学习•ABU.ipynb)\n",
    "11. [附录A 量化环境部署](https://github.com/bbfamily/abu/tree/master/ipython/附录A-量化环境部署.ipynb)\n",
    "12. [附录B 量化相关性分析](https://github.com/bbfamily/abu/tree/master/ipython/附录B-量化相关性分析.ipynb)\n",
    "13. [附录C 量化统计分析及指标应用](https://github.com/bbfamily/abu/tree/master/ipython/附录C-量化统计分析及指标应用.ipynb)\n",
    "\n",
    "[更多阿布量化量化技术文章](http://www.abuquant.com/article)\n",
    "\n",
    "更多关于量化交易相关请阅读[《量化交易之路》](http://www.abuquant.com/books/quantify-trading-road.html)\n",
    "\n",
    "更多关于量化交易与机器学习相关请阅读[《机器学习之路》](http://www.abuquant.com/books/machine-learning-road.html)\n",
    "\n",
    "更多关于abu量化系统请关注微信公众号: abu_quant\n",
    "\n",
    "如有任何问题也可在公众号中联系我的个人微信号。\n",
    "\n",
    "![](./image/qrcode.jpg)"
   ]
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